Chen Chaochao, Su Zhengxian, Zheng Yuwei, Jin Minya, Bi Xiaojie
Department of Laboratory Medicine, Taizhou Hospital of Zhejiang Province Affiliated to Wenzhou Medical University, Linhai, 317000, People's Republic of China.
Infect Drug Resist. 2025 Sep 3;18:4667-4676. doi: 10.2147/IDR.S532869. eCollection 2025.
Sepsis has high mortality and progresses rapidly, requiring early diagnosis; traditional scoring and lab parameters are limited in non-ICU settings, highlighting the need for biomarker integration and continuous monitoring to enhance diagnostic accuracy.
A retrospective analysis of 1,098 patients at Taizhou Hospital of Zhejiang Province identified sepsis and non-sepsis groups per Sepsis 3.0 criteria, Logistic regression analyses were used to identify the risk factors. A dynamic nomogram was built, and predictive accuracy was evaluated using calibration and decision curves. External validation for 94 patients occurred from January to March 2024, using Receiver operating characteristic (ROC) curve analysis for diagnostic evaluation.
Multivariate logistic regression analysis revealed eight independent risk factors significantly associated with sepsis development: hypertension (odds ratio [OR] = 1.6278, 95% confidence interval [CI], 1.2079-2.1937), renal insufficiency (OR=1.7002, 95% CI, 1.2840-2.2513), cardiac insufficiency (OR=1.8927, 95% CI, 1.2979-2.7599), interleukin-6 levels (OR=1.0003 95% CI, 1.0002-1.0005), basophil percentage (OR=0.4319, 95% CI, 0.2353-0.7926), platelet-to-lymphocyte ratio (PLR) (OR=1.0025, 95% CI, 1.0011-1.0040), platelet count (PLT) (OR=0.9939, 95% CI, 0.9912-0.9959) and D-dimer levels (OR=1.0796, 95% CI, 1.0273-1.1347). The prognostic nomogram showed significant discriminative power, with a concordance index of 0.746 (95% CI 0.709-0.772). ROC analysis further revealed a negative predictive value (NPV) of 0.832 and a positive predictive value (PPV) of 0.511. Decision curve analysis validated the clinical utility of the model, demonstrating a substantial net benefit for predicting disease progression within a clinically relevant probability threshold range of 30% - 70%. The model maintained satisfactory discriminative performance in external validation, demonstrating an area under the curve (AUC) of 0.663 (95% CI, 0.549-0.776). The interactive web-based nomogram is available at https://bixiaojie-1987.shinyapps.io/DynNomapp/.
This web-based dynamic nomogram incorporating eight clinically readily available predictors demonstrates robust diagnostic performance for sepsis, which helps doctors make quicker decisions by providing real-time risk assessments for each patient in non-ICU departments.
脓毒症死亡率高且进展迅速,需要早期诊断;传统的评分和实验室参数在非重症监护病房环境中存在局限性,这凸显了整合生物标志物和持续监测以提高诊断准确性的必要性。
对浙江省台州医院的1098例患者进行回顾性分析,根据脓毒症3.0标准确定脓毒症组和非脓毒症组,采用逻辑回归分析确定危险因素。构建动态列线图,并使用校准曲线和决策曲线评估预测准确性。2024年1月至3月对94例患者进行外部验证,采用受试者操作特征(ROC)曲线分析进行诊断评估。
多因素逻辑回归分析显示,有八个独立危险因素与脓毒症发生显著相关:高血压(比值比[OR]=1.6278,95%置信区间[CI],1.2079 - 2.1937)、肾功能不全(OR=1.7002,95% CI,1.2840 - 2.2513)、心功能不全(OR=1.8927,95% CI,1.2979 - 2.7599)、白细胞介素 - 6水平(OR=1.0003,95% CI,1.0002 - 1.0005)、嗜碱性粒细胞百分比(OR=0.4319,95% CI,0.2353 - 0.7926)、血小板与淋巴细胞比值(PLR)(OR=1.0025,95% CI,1.0011 - 1.0040)、血小板计数(PLT)(OR=0.9939,95% CI,0.9912 - 0.9959)和D - 二聚体水平(OR=1.0796,95% CI,1.0273 - 1.1347)。预后列线图显示出显著的判别能力,一致性指数为0.746(95% CI 0.709 - 0.772)。ROC分析进一步显示阴性预测值(NPV)为0.832,阳性预测值(PPV)为0.511。决策曲线分析验证了该模型的临床实用性,表明在30% - 70%的临床相关概率阈值范围内预测疾病进展具有显著的净效益。该模型在外部验证中保持了令人满意的判别性能,曲线下面积(AUC)为0.663(95% CI,0.549 - 0.776)。基于网络的交互式列线图可在https://bixiaojie - 1987.shinyapps.io/DynNomapp/获取。
这种基于网络的动态列线图纳入了八个临床易于获得的预测指标,对脓毒症具有强大的诊断性能,有助于医生通过为非重症监护病房的每位患者提供实时风险评估来更快地做出决策。